Amazon Kinesis vs Amazon Kinesis Firehose vs Google Cloud Dataflow

Amazon Kinesis

501
364
+ 1
3
Amazon Kinesis Firehose

152
111
+ 1
0
Google Cloud Dataflow

131
198
+ 1
0
Pros of Amazon Kinesis
Pros of Amazon Kinesis Firehose
Pros of Google Cloud Dataflow
    No pros available
      No pros available

      Sign up to add or upvote prosMake informed product decisions

      Sign up to add or upvote consMake informed product decisions

      What is Amazon Kinesis?

      Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.

      What is Amazon Kinesis Firehose?

      Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture and automatically load streaming data into Amazon S3 and Amazon Redshift, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today.

      What is Google Cloud Dataflow?

      Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
      What companies use Amazon Kinesis?
      What companies use Amazon Kinesis Firehose?
      What companies use Google Cloud Dataflow?

      Sign up to get full access to all the companiesMake informed product decisions

      What tools integrate with Amazon Kinesis?
      What tools integrate with Amazon Kinesis Firehose?
      What tools integrate with Google Cloud Dataflow?

      Sign up to get full access to all the tool integrationsMake informed product decisions

      What are some alternatives to Amazon Kinesis, Amazon Kinesis Firehose, and Google Cloud Dataflow?
      Kafka
      Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
      Apache Spark
      Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
      Amazon SQS
      Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.
      Firehose.io
      Firehose is both a Rack application and JavaScript library that makes building real-time web applications possible.
      Apache Storm
      Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
      See all alternatives
      Interest over time
      How much does Amazon Kinesis cost?
      How much does Amazon Kinesis Firehose cost?
      How much does Google Cloud Dataflow cost?
      Pricing unavailable
      Pricing unavailable
      News about Google Cloud Dataflow
      More news